Speckle Reducing Contourlet Transform for Medical Ultrasound Images
Speckle noise affects all coherent imaging systems
including medical ultrasound. In medical images, noise suppression
is a particularly delicate and difficult task. A tradeoff between noise
reduction and the preservation of actual image features has to be made
in a way that enhances the diagnostically relevant image content.
Even though wavelets have been extensively used for denoising
speckle images, we have found that denoising using contourlets gives
much better performance in terms of SNR, PSNR, MSE, variance and
correlation coefficient. The objective of the paper is to determine the
number of levels of Laplacian pyramidal decomposition, the number
of directional decompositions to perform on each pyramidal level and
thresholding schemes which yields optimal despeckling of medical
ultrasound images, in particular. The proposed method consists of the
log transformed original ultrasound image being subjected to contourlet
transform, to obtain contourlet coefficients. The transformed
image is denoised by applying thresholding techniques on individual
band pass sub bands using a Bayes shrinkage rule. We quantify the
achieved performance improvement.
[1] Paul Suetens, Fundamentals of Medical Imaging , 1st Edition, Cambridge
University, U.K., pp.145-182, 2002.
[2] N.K.Ragesh, A.R.Anil and R.Rajesh, Digital Image Denoising in Medical
Ultrasound images: A Survey, ICGST AIML-11 Conference, Dubai,
UAE, pp.67-73, 12-14 April 2011.
[3] S. Kalaivani Narayanan and R.S.D.Wahidabanu,A View of Despeckling
in Ultrasound Imaging. Int.J.of Signal Processing, Image processing and
Pattern Recognition, Vol.2,No.3, pp.85-98,2009.
[4] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Visual Enhancement
of Digital Ultrasound Images using Multiscale Wavelet
Domain, Int. J. of Pattern Recognition and Image Analysis , Vol.20,No.3,
pp.303-315, 2010.
[5] M.N. Do and M. Vetterli, The Contourlet Transform: an Efficient
Directional Multiresolution Image Representation, IEEE Transactions
on Image Processing, Vol.14, No.12, pp.20912106, 2005.
[6] S.Satheesh and KVSVR Prasad, Medical Image Denoising Using Adaptive
Threshold Based On Contourlet Transform, Int.J.Advanced Computing,
Vol.2, No.2, pp.52-58, March 2011.
[7] G.Balaji,Image Denoising using Contourlet Transform, RSM Int. J. of
ET & M , Vol.1, pp.40-46,July 2009.
[8] Mao-yu-Huang, yueh-Min Huang and Ming-Shi Wang , Speckle Reduction
of Ultrasound Image Based on Contourlet Transform, Int. Computer
Symposium , Taipei, Taiwan. pp.178-182, Dec. 15-17, 2004.
[9] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Despeckling
Medical Ultrasound Images Using the Contourlet Transform, In: Proceedings
of the 4th AMS Indian International Conference on Artificial
Intelligence Tumkur, Karnataka,India, pp.1814-1827,16-18 Dec 2009.
[10] P.S.Hiremath and Jyothi R.Tegnoor, Automatic Detection of Follicles
in Ultrasound Images of Ovaries, In: Prococeedings of the 2nd International
Conf. on Cognition and Recognition, pp.468-473, April 10-12,
2008.
[11] P.S.Hiremath and Jyothi R. Tegnoor. Contourlet based Method for
Follicle Detection in Ultrasound Images of Ovaries In:proceedings of the
National Seminar on Recent Treands in Image Processing and Pattern
Recognition, pp.114-120, Feb. 15-16, 2010.
[12] P.S.Hiremath and Jyothi R. Tegnoor, Automatic Detection of Follicles in
Ultrasound Images of Ovaries using Edge Based Method, IJCA Special
Issue on Recent Trends in Image Processing and Pattern Recognition
pp.120-125, 2010.
[13] C.B. Burckhardt, Speckle in ultrasound B-mode scans, IEEE Transactions
on Sonics Ultrasonics. Vol.25, No.1, pp.1-6, 1978.
[14] R.F. Wagner, S.W. Smith, J.M. Sandrik, H. Lopez.Statistics of Speckle
in Ultrasound B-scans, IEEE Transactions on SonicsUltrasonics, Vol.30,
pp.156-163, 1983.
[15] J.W. Goodman, Some Fundamental Properties of Speckle, Optics. Soc.
Am. Vol.66 No.11,pp.1145-1149, 1976.
[16] J. C. Bamber and C. Daft Adaptive Filtering for Reduction of Speckle
in Ultrasound Pulse-Echo Images, Ultrasonics pp.41-44. 1986.
[17] V. Dutta,Statistical Analysis of Ultrasound Echo Envelope, Ph.D. dissertation,
Mayo Graduate School,Rochester, MN,1995.
[18] Minh N. Do and Martin Vetterli. Contourlets, In: Beyond Wavelets,
G.V.Well, J Stoeckerand, Academic Press,pp.1-27, 2003.
[19] M.N.Do and M. Vetterli. Framming Pyramids, IEEE Transactions on
Signal Processing , pp. 2329-2342, 2003.
[20] M.N. Do, Contourlets: a new Directional Multiresolution Image Representation,
Conf. Signals Syst. Computer, Vol.1 pp.497501, 2002.
[21] P.J.Burt and E.H. Adelson,The Laplacian Pyramid as a Compact Image
Code, IEEE Transactions on communication Vol.31, pp. 532-540, 1983.
[22] S. Chang, B. Yu and M. Vetterli, Adaptive Wavelet Thresholding
for Image Denoising and Compression, IEEE Transactions on Image
Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.
[1] Paul Suetens, Fundamentals of Medical Imaging , 1st Edition, Cambridge
University, U.K., pp.145-182, 2002.
[2] N.K.Ragesh, A.R.Anil and R.Rajesh, Digital Image Denoising in Medical
Ultrasound images: A Survey, ICGST AIML-11 Conference, Dubai,
UAE, pp.67-73, 12-14 April 2011.
[3] S. Kalaivani Narayanan and R.S.D.Wahidabanu,A View of Despeckling
in Ultrasound Imaging. Int.J.of Signal Processing, Image processing and
Pattern Recognition, Vol.2,No.3, pp.85-98,2009.
[4] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Visual Enhancement
of Digital Ultrasound Images using Multiscale Wavelet
Domain, Int. J. of Pattern Recognition and Image Analysis , Vol.20,No.3,
pp.303-315, 2010.
[5] M.N. Do and M. Vetterli, The Contourlet Transform: an Efficient
Directional Multiresolution Image Representation, IEEE Transactions
on Image Processing, Vol.14, No.12, pp.20912106, 2005.
[6] S.Satheesh and KVSVR Prasad, Medical Image Denoising Using Adaptive
Threshold Based On Contourlet Transform, Int.J.Advanced Computing,
Vol.2, No.2, pp.52-58, March 2011.
[7] G.Balaji,Image Denoising using Contourlet Transform, RSM Int. J. of
ET & M , Vol.1, pp.40-46,July 2009.
[8] Mao-yu-Huang, yueh-Min Huang and Ming-Shi Wang , Speckle Reduction
of Ultrasound Image Based on Contourlet Transform, Int. Computer
Symposium , Taipei, Taiwan. pp.178-182, Dec. 15-17, 2004.
[9] P.S.Hiremath, Prema T. Akkasaligar and Sharan Badiger, Despeckling
Medical Ultrasound Images Using the Contourlet Transform, In: Proceedings
of the 4th AMS Indian International Conference on Artificial
Intelligence Tumkur, Karnataka,India, pp.1814-1827,16-18 Dec 2009.
[10] P.S.Hiremath and Jyothi R.Tegnoor, Automatic Detection of Follicles
in Ultrasound Images of Ovaries, In: Prococeedings of the 2nd International
Conf. on Cognition and Recognition, pp.468-473, April 10-12,
2008.
[11] P.S.Hiremath and Jyothi R. Tegnoor. Contourlet based Method for
Follicle Detection in Ultrasound Images of Ovaries In:proceedings of the
National Seminar on Recent Treands in Image Processing and Pattern
Recognition, pp.114-120, Feb. 15-16, 2010.
[12] P.S.Hiremath and Jyothi R. Tegnoor, Automatic Detection of Follicles in
Ultrasound Images of Ovaries using Edge Based Method, IJCA Special
Issue on Recent Trends in Image Processing and Pattern Recognition
pp.120-125, 2010.
[13] C.B. Burckhardt, Speckle in ultrasound B-mode scans, IEEE Transactions
on Sonics Ultrasonics. Vol.25, No.1, pp.1-6, 1978.
[14] R.F. Wagner, S.W. Smith, J.M. Sandrik, H. Lopez.Statistics of Speckle
in Ultrasound B-scans, IEEE Transactions on SonicsUltrasonics, Vol.30,
pp.156-163, 1983.
[15] J.W. Goodman, Some Fundamental Properties of Speckle, Optics. Soc.
Am. Vol.66 No.11,pp.1145-1149, 1976.
[16] J. C. Bamber and C. Daft Adaptive Filtering for Reduction of Speckle
in Ultrasound Pulse-Echo Images, Ultrasonics pp.41-44. 1986.
[17] V. Dutta,Statistical Analysis of Ultrasound Echo Envelope, Ph.D. dissertation,
Mayo Graduate School,Rochester, MN,1995.
[18] Minh N. Do and Martin Vetterli. Contourlets, In: Beyond Wavelets,
G.V.Well, J Stoeckerand, Academic Press,pp.1-27, 2003.
[19] M.N.Do and M. Vetterli. Framming Pyramids, IEEE Transactions on
Signal Processing , pp. 2329-2342, 2003.
[20] M.N. Do, Contourlets: a new Directional Multiresolution Image Representation,
Conf. Signals Syst. Computer, Vol.1 pp.497501, 2002.
[21] P.J.Burt and E.H. Adelson,The Laplacian Pyramid as a Compact Image
Code, IEEE Transactions on communication Vol.31, pp. 532-540, 1983.
[22] S. Chang, B. Yu and M. Vetterli, Adaptive Wavelet Thresholding
for Image Denoising and Compression, IEEE Transactions on Image
Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.
@article{"International Journal of Information, Control and Computer Sciences:55444", author = "P.S. Hiremath and Prema T. Akkasaligar and Sharan Badiger", title = "Speckle Reducing Contourlet Transform for Medical Ultrasound Images", abstract = "Speckle noise affects all coherent imaging systems
including medical ultrasound. In medical images, noise suppression
is a particularly delicate and difficult task. A tradeoff between noise
reduction and the preservation of actual image features has to be made
in a way that enhances the diagnostically relevant image content.
Even though wavelets have been extensively used for denoising
speckle images, we have found that denoising using contourlets gives
much better performance in terms of SNR, PSNR, MSE, variance and
correlation coefficient. The objective of the paper is to determine the
number of levels of Laplacian pyramidal decomposition, the number
of directional decompositions to perform on each pyramidal level and
thresholding schemes which yields optimal despeckling of medical
ultrasound images, in particular. The proposed method consists of the
log transformed original ultrasound image being subjected to contourlet
transform, to obtain contourlet coefficients. The transformed
image is denoised by applying thresholding techniques on individual
band pass sub bands using a Bayes shrinkage rule. We quantify the
achieved performance improvement.", keywords = "Contourlet transform, Despeckling, Pyramidal directionalfilter bank, Thresholding.", volume = "5", number = "8", pages = "883-8", }
